Metabolic profiling as a tool for revealing<i>Saccharomyces</i>interactions during wine fermentation
Bibliographic record
Abstract
The multi-yeast strain composition of wine fermentations has been well established. However, the effect of multiple strains of Saccharomyces spp. on wine flavour is unknown. Here, we demonstrate that multiple strains of Saccharomyces grown together in grape juice can affect the profile of aroma compounds that accumulate during fermentation. A metabolic footprint of each yeast in monoculture, mixed cultures or blended wines was derived by gas chromatography - mass spectrometry measurement of volatiles accumulated during fermentation. The resultant ion spectrograms were transformed and compared by principal-component analysis. The principal-component analysis showed that the profiles of compounds present in wines made by mixed-culture fermentation were different from those where yeasts were grown in monoculture fermentation, and these differences could not be produced by blending wines. Blending of monoculture wines to mimic the population composition of mixed-culture wines showed that yeast metabolic interactions could account for these differences. Additionally, the yeast strain contribution of volatiles to a mixed fermentation cannot be predicted by the population of that yeast. This study provides a novel way to measure the population status of wine fermentations by metabolic footprinting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".